spanish_definite_articles

libraries

library(tidyverse)

package ‘tidyverse’ was built under R version 4.0.2Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ──────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.1.1     ✓ dplyr   1.0.6
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
package ‘ggplot2’ was built under R version 4.0.2package ‘tibble’ was built under R version 4.0.2package ‘tidyr’ was built under R version 4.0.2package ‘readr’ was built under R version 4.0.2package ‘dplyr’ was built under R version 4.0.2package ‘forcats’ was built under R version 4.0.2── Conflicts ─────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(ggplot2)
library(dbplyr)

package ‘dbplyr’ was built under R version 4.0.2
Attaching package: ‘dbplyr’

The following objects are masked from ‘package:dplyr’:

    ident, sql

library(lmerTest)

package ‘lmerTest’ was built under R version 4.0.2Loading required package: lme4
package ‘lme4’ was built under R version 4.0.2Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack


Attaching package: ‘lmerTest’

The following object is masked from ‘package:lme4’:

    lmer

The following object is masked from ‘package:stats’:

    step

library(languageR)

package ‘languageR’ was built under R version 4.0.2

library(lme4)
library(wesanderson)

package ‘wesanderson’ was built under R version 4.0.2

cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "D55E00", "#CC79A7")
source("helpers.r")

package ‘bootstrap’ was built under R version 4.0.2

data filtering

data = read.csv("main-trials.csv")
demo = read.csv("main-subject_information.csv")

data <- data %>%
  left_join(demo, by="workerid")

data = data %>%
  filter(attention_response != "NA" & item_type != "example3") %>%
  mutate(attention_pass = ifelse(attention_response == attention_correct_response, 1, 0)) %>%
  arrange(workerid)

attention_exclusion = data %>%
  group_by(workerid) %>%
  summarize(accuracy = mean(attention_pass)) %>%
  mutate(exclude = ifelse(accuracy < 0.8, "yes", "no")) %>%
  filter(exclude == "yes")

attention_correct = data %>%
  group_by(workerid) %>%
  summarize(accuracy = mean(attention_pass)) %>%
  mutate(exclude = ifelse(accuracy < 0.8, "yes", "no")) %>%
  filter(exclude == "no")

nonnative = demo %>%
  filter(native != "yes")

experiment completion times before exclusions

data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  ggplot(aes(x=time)) +
  geom_histogram()  +
  xlab("Average completion time") +
  ylab("Number of cases") +
  theme_bw()

Excluding from data frames

data = data[!(data$workerid %in% attention_exclusion$workerid),]
data = data[!(data$workerid %in% nonnative$workerid),]

demo = demo[!(demo$workerid %in% attention_exclusion$workerid),]
demo = demo[!(demo$workerid %in% nonnative$workerid),]

Time exclusions after filtering data based on attention checks and native language

time_exclusions = data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  filter(time < 10)

length(unique(time_exclusions$workerid))

[1] 9

experiment completion times before time exclusions

data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  ggplot(aes(x=time)) +
  geom_histogram()  +
  xlab("Average completion time") +
  ylab("Number of cases") +
  theme_bw()

data = data[!(data$workerid %in% time_exclusions$workerid),]
demo = demo[!(demo$workerid %in% time_exclusions$workerid),]

double check demo and data are the same

length(demo$workerid)

[1] 131

length(unique(data$workerid))

[1] 131

write.csv(demo, "demo.csv")

change character variable to factor variables

data = data %>%
  mutate(age = as.numeric(age)) %>% 
  mutate_if(is.character,as.factor)

NAs introduced by coercion

demo = demo %>%
  mutate(age = as.numeric(age))

NAs introduced by coercion

demo = demo %>%
  mutate_if(is.character,as.factor)

Main data visualizations

Set the background to be white instead of gray.

theme_set(theme_bw())

experiment completion times

data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  ggplot(aes(x=time)) +
  geom_histogram()  +
  xlab("Average completion time") +
  ylab("Number of cases") +
  theme_bw()

Filtering data by critical and filler trials. droplevels() gets rid of unused factor levels (i.e. “filler” in data_critical)

data_critical = data %>%
  filter(trial_type == "critical") %>% 
  droplevels() 

data_filler = data %>% 
  filter(trial_type == "filler")

data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=reorder(context_condition, mean_felicity), y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  scale_fill_manual(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  geom_text(aes(label = mean_felicity), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))

`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing
scale.

ggsave(file = "graphs/felicity_barplot.png",width=5,height=4)

data_critical %>% 
  group_by(DA_presence, context_condition) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=DA_presence, y=mean_felicity, fill = context_condition)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
 #geom_text(aes(label = mean_felicity), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))

`summarise()` has grouped output by 'DA_presence'. You can override using the `.groups` argument.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing
scale.

  data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=reorder(context_condition, mean_affect), y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=affect_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Mean Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  #scale_fill_manual(values = cbPalette) +
  scale_fill_manual(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
 geom_text(aes(label = mean_affect), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))

`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

ggsave(file = "graphs/affect_barplot.png",width=5,height=4)

  data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  ggplot(aes(x = context_condition,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

dodge = position_dodge(.9)

data %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7))

`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

dodge = position_dodge(.9)

data %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7))

`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

data %>% 
  group_by(condition) %>%
  summarize(mean_affect = mean(affect_rating)) %>%
  ggplot(aes(x=reorder(condition,mean_affect), y=mean_affect)) + 
  geom_bar(stat = "identity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.7))

data_critical %>%
  ggplot(aes(x = affect_rating, y = felicity_rating, color = condition)) +
  geom_point() +
  geom_smooth(method="lm") +
  theme_bw()

data %>%
  group_by(condition) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = condition)) +
  geom_point() +
  theme_bw()

data_critical %>%
  group_by(context_condition, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition)) +
  geom_point(size =3) +
  theme_bw()

`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

data_critical %>%
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, group = DA_presence)) +
  geom_point(size =3) +
  geom_smooth(method = "lm") + 
  theme_bw()

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

Variation by item types

data_critical %>%
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, group = DA_presence, label = item_type)) +
  geom_point(size =3) +
  geom_text() +
  geom_smooth(method = "lm")

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

  data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
  facet_wrap(~item_type)

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

  data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  ggplot(aes(x = context_condition,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  facet_wrap(~item_type)

  data_critical %>% 
  group_by(DA_presence, item_type) %>%
  ggplot(aes(x = item_type,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA"))

  data_critical %>% 
  group_by(DA_presence, item_type) %>%
  ggplot(aes(x = item_type,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Affect Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA"))

Plot the felcity and affect ratings for each item_type by using facets.

ggplot(data, aes(x=affect_rating,y=felicity_rating, color=condition)) +
  geom_point() +
  geom_smooth(method="lm") + 
  facet_wrap(~item_type)

Plot the felcity and affect ratings for each item_type by using facets.

ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=condition)) +
  geom_point() +
  #geom_smooth(method="lm") + 
  geom_smooth() + 
  facet_wrap(~item_type)

ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=context_condition)) +
  geom_point() +
  geom_smooth(method="lm") + 
  facet_wrap(~item_type)

ggplot(data_filler, aes(x=affect_rating,y=felicity_rating, color=context_condition)) +
  geom_point() +
  geom_smooth(method="lm") + 
  facet_wrap(~item_type)

ggsave(file = "graphs/DA_by_item_type.png",width=10,height=7)

ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
  geom_point() +
  geom_smooth(method="lm") + 
  #geom_smooth() + 
  labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
  #scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  scale_color_manual(values = cbPalette) +
  facet_wrap(~referent_gender)

ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
  geom_point() +
  geom_smooth(method="lm") + 
  #geom_smooth() + 
  labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
  #scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  scale_color_manual(values = cbPalette) +
  facet_grid(rows = vars(item_type), cols = vars(referent_gender))

dodge = position_dodge(.9)

data_critical %>% 
  group_by(referent_gender, DA_presence, item_type) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=referent_gender, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~item_type)

`summarise()` has grouped output by 'referent_gender', 'DA_presence'. You can override using the `.groups` argument.

  data_critical %>% 
  group_by(DA_presence, referent_gender) %>%
  ggplot(aes(x = referent_gender,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  #labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

  data_critical %>% 
  group_by(DA_presence, referent_gender) %>%
  ggplot(aes(x = referent_gender,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  #labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~item_type)

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~item_type)

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

Exploratory data visualizations for social variables

data %>%
  group_by(region_country) %>%
  ggplot(aes(x = fct_infreq(region_country), fill=region_country)) +
  geom_bar(stat = 'count') +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.7)) +
  scale_fill_manual(values = wes_palette("Zissou1", 15, type = "continuous")) +
  theme(legend.position = "none")

demo %>%
  group_by(region_country) %>%
  ggplot(aes(x = fct_infreq(region_country), fill=region_country)) +
  geom_bar(stat = 'count') +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.7)) +
  scale_fill_manual(values = wes_palette("Zissou1", 15, type = "continuous")) +
  theme(legend.position = "none") +
  labs (x="Country", y= "Number of Participants")

ggsave(file = "graphs/participants_country.png",width=5,height=4)

dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence, region_country) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~region_country)

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
  group_by(context_condition, DA_presence, region_country) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~region_country) +
  theme_bw()

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
  group_by(context_condition, DA_presence, region_country) %>%
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>% 
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  #filter(trial_sum > 25) %>% 
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~region_country) +
  theme_bw()

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

  data_critical %>% 
  group_by(context_condition, DA_presence, region_country) %>%
  ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "CG", "neutralNoCG" = "No CG")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  facet_wrap(~region_country)

data_critical %>% 
  group_by(context_condition, DA_presence, region_country) %>%
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~region_country)

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

  data_critical %>% 
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  group_by(DA_presence, region_country) %>%
  ggplot(aes(x = region_country,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Country", y= "Felicity Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

ggsave(file = "graphs/country_felicity_boxplot.png",width=7,height=5)

  data_critical %>% 
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  group_by(condition, region_country) %>%
  ggplot(aes(x = region_country,y=felicity_rating, fill = condition)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence")

  #scale_fill_discrete(labels=c("DA", "no DA"))

  data_critical %>% 
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  group_by(DA_presence, region_country) %>%
  ggplot(aes(x = region_country,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Country", y= "Affect Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA")) +
 scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

ggsave(file = "graphs/country_affect_boxplot.png",width=7,height=5)

data_critical %>%
  group_by(context_condition, DA_presence, education) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~education) +
  theme_bw()

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
  filter(!is.na(age)) %>% 
  group_by(workerid, age, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = age, y = mean_felicity, color = DA_presence)) +
  geom_point(size =3) +
  geom_smooth(method = "lm") +
  theme_bw()

`summarise()` has grouped output by 'workerid', 'age'. You can override using the `.groups` argument.

data_critical %>%
  filter(!is.na(age)) %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(workerid, age) %>%
  summarize(reported_usage = paste(unique(reported_usage))) %>%
  ggplot(aes(x = age, y = as.numeric(reported_usage))) +
  geom_point(size =3) +
  geom_smooth(method = "lm") +
  theme_bw()

`summarise()` has grouped output by 'workerid'. You can override using the `.groups` argument.

data_critical %>%
  filter(!is.na(age)) %>% 
  group_by(workerid, age, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = age, y = mean_affect, color = DA_presence)) +
  geom_point(size =3) +
  geom_smooth(method = "lm") +
  geom_hline(yintercept = 0.5) +
  theme_bw()

`summarise()` has grouped output by 'workerid', 'age'. You can override using the `.groups` argument.

data_critical %>%
  group_by(context_condition, DA_presence, reported_usage) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~reported_usage)

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(condition, reported_usage) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x= reorder(reported_usage, mean_felicity), y=mean_felicity, fill = condition)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7))

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

dodge = position_dodge(.9)

data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(DA_presence, reported_usage, context_condition) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~ reported_usage)

`summarise()` has grouped output by 'DA_presence', 'reported_usage'. You can override using the `.groups` argument.

dodge = position_dodge(.9)

data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(DA_presence, reported_usage, context_condition) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")) +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~ reported_usage)

`summarise()` has grouped output by 'DA_presence', 'reported_usage'. You can override using the `.groups` argument.

data_critical %>%
  group_by(context_condition, DA_presence, reported_familiarity) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~reported_familiarity)

`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

  data_critical %>% 
  #filter(reported_familiarity == "0" | reported_familiarity == "1" | reported_familiarity == "2" | reported_familiarity == "3" ) %>%
  group_by(DA_presence, reported_familiarity) %>%
  ggplot(aes(x = reported_familiarity,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA"))

  data_critical %>% 
  #filter(reported_familiarity == "0" | reported_familiarity == "1" | reported_familiarity == "2" | reported_familiarity == "3") %>%
  group_by(DA_presence, reported_familiarity) %>%
  ggplot(aes(x = reported_familiarity,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Affect Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA"))

Data modeling

Releveling (changing reference level) and centering DA predictor and context condition predictor

data_model = data_critical %>% 
  mutate(context_condition = fct_relevel(context_condition, "neutralNoCG")) %>% 
  mutate(numDA_presence = as.numeric(DA_presence), numcontext_condition = as.numeric(context_condition)) %>% 
  mutate(cDA_presence = scale(numDA_presence), cContext_condition = scale(numcontext_condition))

1. (OSF preregistered analysis) Linear mixed effects analysis, predicting felicity from a centered presence of the DA predictor and a centered context condition predictor. By subject and by item, random intercepts as well as random slopes for condition and DA presence.

felicity_mdl <- lmer(felicity_rating ~ cDA_presence*cContext_condition + (1 + cDA_presence + cContext_condition|workerid) + (1 + cDA_presence + cContext_condition|item_type), data=data_model, REML=F)
summary(felicity_mdl)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ cDA_presence * cContext_condition + (1 + cDA_presence +  
    cContext_condition | workerid) + (1 + cDA_presence + cContext_condition |      item_type)
   Data: data_model

     AIC      BIC   logLik deviance df.resid 
   329.1    413.3   -147.5    295.1     1031 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.9840 -0.5603  0.1028  0.5781  2.9673 

Random effects:
 Groups    Name               Variance Std.Dev. Corr       
 workerid  (Intercept)        0.022473 0.14991             
           cDA_presence       0.011299 0.10630  -0.68      
           cContext_condition 0.004487 0.06699  -0.27 -0.35
 item_type (Intercept)        0.001935 0.04398             
           cDA_presence       0.000970 0.03115   0.26      
           cContext_condition 0.001135 0.03369  -0.77  0.25
 Residual                     0.055257 0.23507             
Number of obs: 1048, groups:  workerid, 131; item_type, 8

Fixed effects:
                                 Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                       0.62679    0.02168  18.34441  28.913  < 2e-16 ***
cDA_presence                      0.10514    0.01629  16.18323   6.456  7.5e-06 ***
cContext_condition                0.07247    0.01531  10.20344   4.734 0.000757 ***
cDA_presence:cContext_condition   0.02223    0.00777 858.01487   2.862 0.004317 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) cDA_pr cCntx_
cDA_presenc -0.106              
cCntxt_cndt -0.494  0.059       
cDA_prsn:C_ -0.002 -0.018 -0.002

Simple regressions to analyze interaction in felicity_mdl

summary(felicity_mdl.simple)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ DA_presence * context_condition - context_condition +  
    (1 + DA_presence + context_condition | workerid) + (1 + DA_presence +  
    context_condition | item_type)
   Data: data_model

     AIC      BIC   logLik deviance df.resid 
   329.1    413.3   -147.5    295.1     1031 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.9840 -0.5603  0.1028  0.5780  2.9672 

Random effects:
 Groups    Name                       Variance Std.Dev. Corr       
 workerid  (Intercept)                0.051928 0.22788             
           DA_presencenoDA            0.048676 0.22063  -0.69      
           context_conditionneutralCG 0.017948 0.13397  -0.36 -0.35
 item_type (Intercept)                0.005925 0.07697             
           DA_presencenoDA            0.004175 0.06462  -0.27      
           context_conditionneutralCG 0.004544 0.06741  -0.97  0.25
 Residual                             0.055257 0.23507             
Number of obs: 1048, groups:  workerid, 131; item_type, 8

Fixed effects:
                                           Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)                                 0.48972    0.03646 17.85773  13.430 9.01e-11 ***
DA_presencenoDA                             0.17067    0.03792 25.57704   4.500  0.00013 ***
DA_presenceDA:context_conditionneutralCG    0.11122    0.03284 13.47242   3.386  0.00465 ** 
DA_presencenoDA:context_conditionneutralCG  0.20352    0.03680 21.11904   5.531 1.70e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                (Intr) DA_pDA DA_prsncDA:c_CG
DA_prsncnDA     -0.459                       
DA_prsncDA:c_CG -0.759  0.213                
DA_prsncnDA:_CG -0.526 -0.208  0.576         
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00230512 (tol = 0.002, component 1)

summary(felicity_mdl.simple1.2)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ DA_presence * context_condition - context_condition +  
    (1 + DA_presence + context_condition | workerid) + (1 | item_type)
   Data: data_model

     AIC      BIC   logLik deviance df.resid 
   339.9    399.4   -158.0    315.9     1036 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.1959 -0.5365  0.1418  0.5975  3.0964 

Random effects:
 Groups    Name                       Variance Std.Dev. Corr       
 workerid  (Intercept)                0.052982 0.23018             
           DA_presencenoDA            0.048529 0.22029  -0.69      
           context_conditionneutralCG 0.016746 0.12941  -0.35 -0.38
 item_type (Intercept)                0.002071 0.04551             
 Residual                             0.057499 0.23979             
Number of obs: 1048, groups:  workerid, 131; item_type, 8

Fixed effects:
                                            Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                                  0.48721    0.02934  48.65604  16.607  < 2e-16 ***
DA_presencenoDA                              0.17455    0.03041 264.31048   5.740 2.59e-08 ***
DA_presenceDA:context_conditionneutralCG     0.11187    0.02267 238.40614   4.935 1.51e-06 ***
DA_presencenoDA:context_conditionneutralCG   0.20006    0.02806 450.44717   7.130 4.02e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                (Intr) DA_pDA DA_prsncDA:c_CG
DA_prsncnDA     -0.526                       
DA_prsncDA:c_CG -0.424  0.184                
DA_prsncnDA:_CG -0.092 -0.511  0.191         

felicity_mdl.simple2 <- lmer(felicity_rating ~ context_condition*DA_presence - DA_presence + (1 + DA_presence + context_condition|workerid) + (1 + DA_presence + context_condition|item_type), data=data_model, REML=F)
summary(felicity_mdl.simple2)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ context_condition * DA_presence - DA_presence +  
    (1 + DA_presence + context_condition | workerid) + (1 + DA_presence +      context_condition | item_type)
   Data: data_model

     AIC      BIC   logLik deviance df.resid 
   329.1    413.3   -147.5    295.1     1031 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.9838 -0.5604  0.1028  0.5781  2.9673 

Random effects:
 Groups    Name                       Variance Std.Dev. Corr       
 workerid  (Intercept)                0.051932 0.22789             
           DA_presencenoDA            0.048681 0.22064  -0.69      
           context_conditionneutralCG 0.017942 0.13395  -0.36 -0.35
 item_type (Intercept)                0.005926 0.07698             
           DA_presencenoDA            0.004182 0.06467  -0.27      
           context_conditionneutralCG 0.004543 0.06740  -0.97  0.26
 Residual                             0.055258 0.23507             
Number of obs: 1048, groups:  workerid, 131; item_type, 8

Fixed effects:
                                             Estimate Std. Error       df t value Pr(>|t|)    
(Intercept)                                   0.48972    0.03647 17.85360  13.429 9.05e-11 ***
context_conditionneutralCG                    0.11122    0.03284 13.47111   3.386 0.004651 ** 
context_conditionneutralNoCG:DA_presencenoDA  0.17067    0.03793 25.51918   4.499 0.000131 ***
context_conditionneutralCG:DA_presencenoDA    0.26297    0.03699 23.06763   7.109 3.01e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) cnt_CG c_NCG:
cntxt_cndCG -0.759              
c_NCG:DA_DA -0.459  0.213       
cn_CG:DA_DA -0.321 -0.096  0.630

2. Linear mixed effects analysis, predicting affect from a centered presence of DA predictor and and a centered context condition predictor. By subject and by item, random intercepts as well as random slopes for condition and DA presence.

affect_mdl <- lmer(affect_rating ~ cDA_presence*cContext_condition + (1 + cDA_presence + cContext_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: affect_rating ~ cDA_presence * cContext_condition + (1 + cDA_presence +  
    cContext_condition | workerid) + (1 | item_type)
   Data: data_model

     AIC      BIC   logLik deviance df.resid 
  -616.3   -556.9    320.2   -640.3     1036 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.9958 -0.5019  0.0226  0.5831  2.9014 

Random effects:
 Groups    Name               Variance  Std.Dev. Corr       
 workerid  (Intercept)        0.0170904 0.13073             
           cDA_presence       0.0031298 0.05594  -0.34      
           cContext_condition 0.0005461 0.02337   0.25 -0.27
 item_type (Intercept)        0.0011279 0.03358             
 Residual                     0.0220981 0.14865             
Number of obs: 1048, groups:  workerid, 131; item_type, 8

Fixed effects:
                                 Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                     6.664e-01  1.713e-02 2.370e+01  38.896  < 2e-16 ***
cDA_presence                    2.637e-02  6.859e-03 1.306e+02   3.845 0.000187 ***
cContext_condition              1.755e-02  5.255e-03 1.337e+02   3.340 0.001086 ** 
cDA_presence:cContext_condition 4.261e-03  4.887e-03 8.595e+02   0.872 0.383523    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) cDA_pr cCntx_
cDA_presenc -0.160              
cCntxt_cndt  0.065 -0.065       
cDA_prsn:C_  0.002  0.001  0.010

affect_mdl.simple <- lmer(affect_rating ~ DA_presence*context_condition - context_condition + (1 + DA_presence + context_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl.simple)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: affect_rating ~ DA_presence * context_condition - context_condition +  
    (1 + DA_presence + context_condition | workerid) + (1 | item_type)
   Data: data_model

     AIC      BIC   logLik deviance df.resid 
  -616.3   -556.9    320.2   -640.3     1036 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.9958 -0.5019  0.0226  0.5831  2.9014 

Random effects:
 Groups    Name                       Variance Std.Dev. Corr       
 workerid  (Intercept)                0.021142 0.14540             
           DA_presencenoDA            0.013485 0.11612  -0.56      
           context_conditionneutralCG 0.002185 0.04674   0.14 -0.27
 item_type (Intercept)                0.001128 0.03359             
 Residual                             0.022098 0.14865             
Number of obs: 1048, groups:  workerid, 131; item_type, 8

Fixed effects:
                                            Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                                  0.63168    0.01942  37.13365  32.528  < 2e-16 ***
DA_presencenoDA                              0.04562    0.01765 281.54763   2.585  0.01024 *  
DA_presenceDA:context_conditionneutralCG     0.02864    0.01280 258.42126   2.237  0.02612 *  
DA_presencenoDA:context_conditionneutralCG   0.04633    0.01670 519.65179   2.774  0.00574 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
                (Intr) DA_pDA DA_prsncDA:c_CG
DA_prsncnDA     -0.431                       
DA_prsncDA:c_CG -0.279  0.295                
DA_prsncnDA:_CG  0.024 -0.492  0.073         

affect_mdl.simple2 <- lmer(affect_rating ~ context_condition*DA_presence - DA_presence + (1 + DA_presence + context_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl.simple2)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: affect_rating ~ context_condition * DA_presence - DA_presence +  
    (1 + DA_presence + context_condition | workerid) + (1 | item_type)
   Data: data_model

     AIC      BIC   logLik deviance df.resid 
  -616.3   -556.9    320.2   -640.3     1036 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.9958 -0.5019  0.0226  0.5831  2.9014 

Random effects:
 Groups    Name                       Variance Std.Dev. Corr       
 workerid  (Intercept)                0.021142 0.14540             
           DA_presencenoDA            0.013484 0.11612  -0.56      
           context_conditionneutralCG 0.002185 0.04674   0.14 -0.27
 item_type (Intercept)                0.001128 0.03359             
 Residual                             0.022098 0.14865             
Number of obs: 1048, groups:  workerid, 131; item_type, 8

Fixed effects:
                                              Estimate Std. Error        df t value Pr(>|t|)    
(Intercept)                                    0.63168    0.01942  37.14235  32.529  < 2e-16 ***
context_conditionneutralCG                     0.02864    0.01280 258.43091   2.237 0.026123 *  
context_conditionneutralNoCG:DA_presencenoDA   0.04562    0.01765 281.55716   2.585 0.010241 *  
context_conditionneutralCG:DA_presencenoDA     0.06331    0.01731 259.06531   3.657 0.000309 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) cnt_CG c_NCG:
cntxt_cndCG -0.279              
c_NCG:DA_DA -0.431  0.295       
cn_CG:DA_DA -0.210 -0.369  0.327

summary(region_mdl)

Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ cDA_presence * cContext_condition + region_country +  
    (1 + cDA_presence + cContext_condition | workerid) + (1 +  
    cDA_presence + cContext_condition | item_type)
   Data: data_region

     AIC      BIC   logLik deviance df.resid 
   238.3    337.3    -98.1    196.3      803 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2679 -0.5425  0.1101  0.5849  3.0926 

Random effects:
 Groups    Name               Variance  Std.Dev. Corr       
 workerid  (Intercept)        0.0223542 0.14951             
           cDA_presence       0.0093433 0.09666  -0.71      
           cContext_condition 0.0046385 0.06811  -0.35 -0.36
 item_type (Intercept)        0.0029706 0.05450             
           cDA_presence       0.0007791 0.02791   0.33      
           cContext_condition 0.0007621 0.02761  -0.97 -0.10
 Residual                     0.0534671 0.23123             
Number of obs: 824, groups:  workerid, 103; item_type, 8

Fixed effects:
                                  Estimate Std. Error         df t value Pr(>|t|)    
(Intercept)                       0.678572   0.040601  67.069214  16.713  < 2e-16 ***
cDA_presence                      0.114982   0.016062  16.895281   7.159 1.66e-06 ***
cContext_condition                0.080439   0.014557  12.906910   5.526   0.0001 ***
region_countrymexico             -0.038139   0.050308 102.491854  -0.758   0.4501    
region_countryspain              -0.070743   0.046598  98.609101  -1.518   0.1322    
region_countryusa                -0.079151   0.041890  96.141961  -1.890   0.0618 .  
region_countryvenezuela          -0.070523   0.052186 103.760639  -1.351   0.1795    
cDA_presence:cContext_condition   0.018369   0.008563 675.287983   2.145   0.0323 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) cDA_pr cCntx_ rgn_cntrym rgn_cntrysp regn_cntrys rgn_cntryv
cDA_presenc -0.058                                                            
cCntxt_cndt -0.379 -0.140                                                     
rgn_cntrymx -0.585  0.001  0.019                                              
rgn_cntrysp -0.629  0.003  0.012  0.507                                       
regn_cntrys -0.697 -0.002  0.005  0.564      0.606                            
rgn_cntryvn -0.561  0.007  0.006  0.451      0.486       0.541                
cDA_prsn:C_ -0.020 -0.026 -0.010  0.034      0.029       0.010      -0.005